arXiv:2606.24618v1 Announce Type: new Abstract: In ontology-based data access (OBDA), multiple data sources are integrated via mappings to an ontology. We consider an OBDA setting based on existential rules and the certain answer semantics. We address the recent issue of query abstraction, which consists of abstracting data queries by translating them to the ontology layer. Since a perfect abstraction may not exist, the notions of minimally complete and maximally sound abstractions have been introduced. We study abstractions within an extension of UCQs with a limited form of inequality and a s
This publication represents continued academic research into fundamental aspects of ontology-based data access (OBDA) and query abstraction, pushing the theoretical boundaries of integrating disparate data sources for AI applications.
Improving query abstraction in OBDA can lead to more robust and efficient AI systems that can seamlessly integrate and reason over complex, distributed datasets, accelerating enterprise AI adoption.
The theoretical understanding of how AI systems can more accurately and efficiently abstract queries across diverse data sources is incrementally enhanced, potentially leading to more advanced data integration techniques.
- · AI/ML researchers
- · Enterprise data management platforms
- · Knowledge graph developers
- · Organizations with siloed, unintelligible data
Refined query abstraction methods will improve the effectiveness of AI systems in handling varied data structures and ontologies.
Enhanced data integration capabilities could accelerate the development and deployment of more sophisticated AI agents in complex environments.
As AI becomes better at abstracting and integrating information, the value of bespoke, human-curated data mappings might diminish, shifting focus to automated schema alignment.
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Read at arXiv cs.AI